{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "806abb9d",
   "metadata": {},
   "source": [
    "# Installation Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2e26fb19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0fbb7552",
   "metadata": {
    "id": "fdaf745f",
    "outputId": "e21f96c1-a8fb-4895-84a3-69772f3c876c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import dask.dataframe as dd\n",
    "from scipy import stats\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.regression.rolling import RollingOLS\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import ticker\n",
    "import seaborn as sns; sns.set()\n",
    "from pandas.tseries.offsets import *\n",
    "from dateutil.relativedelta import *\n",
    "import datetime as dt\n",
    "import os\n",
    "from linearmodels.asset_pricing import TradedFactorModel, LinearFactorModel\n",
    "from IPython.core.pylabtools import figsize\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "from fredapi import Fred\n",
    "fred = Fred(api_key = 'b0363f9c9d853b92b27e06c4727bc2ea')\n",
    "import pandas_datareader.data as web\n",
    "\n",
    "%matplotlib inline \n",
    "%pylab inline\n",
    "pylab.rcParams['figure.figsize'] = (20,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ccd1845b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "from multiprocessing import Pool \n",
    "import random\n",
    "import json\n",
    "import sys\n",
    "import StockPortfolioEnv\n",
    "\n",
    "import pytz\n",
    "import itertools\n",
    "from datetime import datetime as dt\n",
    "from finrl.meta.preprocessor.yahoodownloader import YahooDownloader\n",
    "from finrl.meta.preprocessor.preprocessors import FeatureEngineer, data_split\n",
    "from finrl import config\n",
    "from finrl import config_tickers\n",
    "from finrl.config import (\n",
    "    DATA_SAVE_DIR,\n",
    "    TRAINED_MODEL_DIR,\n",
    "    TENSORBOARD_LOG_DIR,\n",
    "    RESULTS_DIR,\n",
    "    INDICATORS,\n",
    "    TRAIN_START_DATE,\n",
    "    TRAIN_END_DATE,\n",
    "    TEST_START_DATE,\n",
    "    TEST_END_DATE,\n",
    "    TRADE_START_DATE,\n",
    "    TRADE_END_DATE,\n",
    ")\n",
    "\n",
    "if not os.path.exists(\"./\" + config.RESULTS_DIR):\n",
    "    os.makedirs(\"./\" + config.RESULTS_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a827d804",
   "metadata": {
    "id": "13941034"
   },
   "outputs": [],
   "source": [
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "pd.options.display.float_format = '{:,.3f}'.format\n",
    "pd.set_option('mode.use_inf_as_na', True)\n",
    "pd.set_option('display.max_columns', 300)\n",
    "pd.set_option('display.max_rows', 500)\n",
    "idx = pd.IndexSlice\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54129501",
   "metadata": {
    "id": "8c813139"
   },
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "56d6354d",
   "metadata": {
    "id": "1c2653fa"
   },
   "outputs": [],
   "source": [
    "Datasets = os.path.dirname(os.getcwd()) + \"\\\\data\\\\\"\n",
    "__depends__ = [Datasets+\"RetailMarketOrder.sas7bdat\", \n",
    "               Datasets+\"InstitutionOrder.sas7bdat\", \n",
    "               Datasets+\"FutureReturn.sas7bdat\"]\n",
    "__dest__ = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "dd800519",
   "metadata": {
    "id": "67b58dbb",
    "outputId": "1d7619ee-b582-4980-b5e2-f89e1038c612"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['D:\\\\Google Drive - Columbia University\\\\AI4Finance\\\\Imitation Learning\\\\data\\\\RetailMarketOrder.sas7bdat',\n",
       " 'D:\\\\Google Drive - Columbia University\\\\AI4Finance\\\\Imitation Learning\\\\data\\\\InstitutionOrder.sas7bdat',\n",
       " 'D:\\\\Google Drive - Columbia University\\\\AI4Finance\\\\Imitation Learning\\\\data\\\\FutureReturn.sas7bdat']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "__depends__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d1d35b0",
   "metadata": {},
   "source": [
    "## Column Details"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "423d3a8d",
   "metadata": {
    "id": "392daeed"
   },
   "source": [
    "- permno is the permanent stock identifier used in CRSP\n",
    "- Retail Market Order Imbalance (moribvol) = (Shares Purchased - Shares Sold)/(Shares Purchased+Shares Sold)\n",
    "    - We standardize by dividing the sum of total buy and sell retail market orders to make this variable comparable across different stocks\n",
    "    - Tracking Retail Investor Activity: https://onlinelibrary.wiley.com/doi/abs/10.1111/jofi.13033\n",
    "- 5 Russell groups\n",
    "    - Classification of stocks according to the constituent members of Russell indices\n",
    "    - Large-Cap (russellgroup = 1): stocks in Russell Top 200, which consists of the largest 200 members in Russell 1000\n",
    "    - Mid-Cap (russellgroup = 2): stocks in Russell Mid-Cap, which consists of the smallest 800 members in Russell 1000\n",
    "    - Small-Cap (russellgroup = 3): the largest 1000 members in Russell 2000\n",
    "    - Micro-Cap (russellgroup = 4): stocks in Russell Micro-Cap, which consists of the smallest 1000 members in Russell 2000 plus the largest 1000 stocks outside Russell 2000\n",
    "    - Nano-Cap (russellgroup = 5): all remaining stocks\n",
    "- 11 sectors\n",
    "    - The Global Industry Classification Standard (GICS)\n",
    "    - I name a sector using the corresponding ticker of the SPDR sector ETF (see, https://www.sectorspdr.com/sectorspdr/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "92bb03a2",
   "metadata": {
    "id": "8afcf2c3",
    "outputId": "62055389-60a9-4510-935e-dec44dca6988"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>permno</th>\n",
       "      <th>ticker</th>\n",
       "      <th>companyname</th>\n",
       "      <th>date</th>\n",
       "      <th>russellgroup</th>\n",
       "      <th>sector</th>\n",
       "      <th>price</th>\n",
       "      <th>marketcap</th>\n",
       "      <th>moribvol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14431820</th>\n",
       "      <td>93436</td>\n",
       "      <td>TSLA</td>\n",
       "      <td>TESLA INC</td>\n",
       "      <td>2021-12-27</td>\n",
       "      <td>1</td>\n",
       "      <td>XLY</td>\n",
       "      <td>1,093.940</td>\n",
       "      <td>1,098,605.595</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14431821</th>\n",
       "      <td>93436</td>\n",
       "      <td>TSLA</td>\n",
       "      <td>TESLA INC</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>1</td>\n",
       "      <td>XLY</td>\n",
       "      <td>1,088.470</td>\n",
       "      <td>1,093,112.295</td>\n",
       "      <td>-0.021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14431822</th>\n",
       "      <td>93436</td>\n",
       "      <td>TSLA</td>\n",
       "      <td>TESLA INC</td>\n",
       "      <td>2021-12-29</td>\n",
       "      <td>1</td>\n",
       "      <td>XLY</td>\n",
       "      <td>1,086.190</td>\n",
       "      <td>1,090,822.542</td>\n",
       "      <td>-0.033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14431823</th>\n",
       "      <td>93436</td>\n",
       "      <td>TSLA</td>\n",
       "      <td>TESLA INC</td>\n",
       "      <td>2021-12-30</td>\n",
       "      <td>1</td>\n",
       "      <td>XLY</td>\n",
       "      <td>1,070.340</td>\n",
       "      <td>1,074,904.966</td>\n",
       "      <td>-0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14431824</th>\n",
       "      <td>93436</td>\n",
       "      <td>TSLA</td>\n",
       "      <td>TESLA INC</td>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>XLY</td>\n",
       "      <td>1,056.780</td>\n",
       "      <td>1,092,190.615</td>\n",
       "      <td>-0.087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          permno ticker companyname       date  russellgroup sector     price  \\\n",
       "14431820   93436   TSLA   TESLA INC 2021-12-27             1    XLY 1,093.940   \n",
       "14431821   93436   TSLA   TESLA INC 2021-12-28             1    XLY 1,088.470   \n",
       "14431822   93436   TSLA   TESLA INC 2021-12-29             1    XLY 1,086.190   \n",
       "14431823   93436   TSLA   TESLA INC 2021-12-30             1    XLY 1,070.340   \n",
       "14431824   93436   TSLA   TESLA INC 2021-12-31             1    XLY 1,056.780   \n",
       "\n",
       "             marketcap  moribvol  \n",
       "14431820 1,098,605.595     0.001  \n",
       "14431821 1,093,112.295    -0.021  \n",
       "14431822 1,090,822.542    -0.033  \n",
       "14431823 1,074,904.966    -0.007  \n",
       "14431824 1,092,190.615    -0.087  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retail = pd.read_sas(__depends__[0], encoding = 'latin-1')\n",
    "retail.columns = retail.columns.str.lower()\n",
    "retail[['permno','russellgroup']] = retail[['permno','russellgroup']].astype(int)\n",
    "retail.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8a8c1460",
   "metadata": {
    "id": "fde23cdc",
    "outputId": "8eabce7a-6f9a-44ec-c566-391f0b87cd74"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "retail.groupby(['date','sector'])['permno'].count().unstack().plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a4a5bfd2",
   "metadata": {
    "id": "973a6be3",
    "outputId": "eb7d08b5-33cd-40db-fe0a-c238c01bf15e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>permno</th>\n",
       "      <th>ticker</th>\n",
       "      <th>companyname</th>\n",
       "      <th>date</th>\n",
       "      <th>russellgroup</th>\n",
       "      <th>sector</th>\n",
       "      <th>price</th>\n",
       "      <th>marketcap</th>\n",
       "      <th>iibvol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10772976</th>\n",
       "      <td>93436</td>\n",
       "      <td>TSLA</td>\n",
       "      <td>TESLA MOTORS INC</td>\n",
       "      <td>2011-03-25</td>\n",
       "      <td>2</td>\n",
       "      <td>XLY</td>\n",
       "      <td>22.750</td>\n",
       "      <td>2,168.871</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10772977</th>\n",
       "      <td>93436</td>\n",
       "      <td>TSLA</td>\n",
       "      <td>TESLA MOTORS INC</td>\n",
       "      <td>2011-03-28</td>\n",
       "      <td>2</td>\n",
       "      <td>XLY</td>\n",
       "      <td>23.250</td>\n",
       "      <td>2,216.539</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10772978</th>\n",
       "      <td>93436</td>\n",
       "      <td>TSLA</td>\n",
       "      <td>TESLA MOTORS INC</td>\n",
       "      <td>2011-03-29</td>\n",
       "      <td>2</td>\n",
       "      <td>XLY</td>\n",
       "      <td>23.920</td>\n",
       "      <td>2,280.413</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10772979</th>\n",
       "      <td>93436</td>\n",
       "      <td>TSLA</td>\n",
       "      <td>TESLA MOTORS INC</td>\n",
       "      <td>2011-03-30</td>\n",
       "      <td>2</td>\n",
       "      <td>XLY</td>\n",
       "      <td>23.710</td>\n",
       "      <td>2,260.393</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10772980</th>\n",
       "      <td>93436</td>\n",
       "      <td>TSLA</td>\n",
       "      <td>TESLA MOTORS INC</td>\n",
       "      <td>2011-03-31</td>\n",
       "      <td>2</td>\n",
       "      <td>XLY</td>\n",
       "      <td>27.750</td>\n",
       "      <td>2,651.651</td>\n",
       "      <td>-1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          permno ticker       companyname       date  russellgroup sector  \\\n",
       "10772976   93436   TSLA  TESLA MOTORS INC 2011-03-25             2    XLY   \n",
       "10772977   93436   TSLA  TESLA MOTORS INC 2011-03-28             2    XLY   \n",
       "10772978   93436   TSLA  TESLA MOTORS INC 2011-03-29             2    XLY   \n",
       "10772979   93436   TSLA  TESLA MOTORS INC 2011-03-30             2    XLY   \n",
       "10772980   93436   TSLA  TESLA MOTORS INC 2011-03-31             2    XLY   \n",
       "\n",
       "          price  marketcap  iibvol  \n",
       "10772976 22.750  2,168.871   0.000  \n",
       "10772977 23.250  2,216.539   1.000  \n",
       "10772978 23.920  2,280.413   0.000  \n",
       "10772979 23.710  2,260.393   0.000  \n",
       "10772980 27.750  2,651.651  -1.000  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "institution = pd.read_sas(__depends__[1], encoding = 'latin-1')\n",
    "institution.columns = institution.columns.str.lower()\n",
    "institution[['permno','russellgroup']] = institution[['permno','russellgroup']].astype(int)\n",
    "institution.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f514dfb8",
   "metadata": {
    "id": "c2468f22",
    "outputId": "59abf57c-f90e-49a1-dc13-1b6a14f80e01"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "institution.groupby(['date','sector'])['permno'].count().unstack().plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "dfc605b8",
   "metadata": {
    "id": "08d82e23",
    "outputId": "8a4b05f1-0aca-46e8-da49-10af455f349f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>permno</th>\n",
       "      <th>date</th>\n",
       "      <th>ret_1</th>\n",
       "      <th>ret_5</th>\n",
       "      <th>ret_10</th>\n",
       "      <th>ret_20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21466675</th>\n",
       "      <td>93436</td>\n",
       "      <td>2021-12-27</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.097</td>\n",
       "      <td>-0.033</td>\n",
       "      <td>-0.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21466676</th>\n",
       "      <td>93436</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.056</td>\n",
       "      <td>-0.022</td>\n",
       "      <td>-0.139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21466677</th>\n",
       "      <td>93436</td>\n",
       "      <td>2021-12-29</td>\n",
       "      <td>-0.015</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.018</td>\n",
       "      <td>-0.237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21466678</th>\n",
       "      <td>93436</td>\n",
       "      <td>2021-12-30</td>\n",
       "      <td>-0.013</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>-0.036</td>\n",
       "      <td>-0.209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21466679</th>\n",
       "      <td>93436</td>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>0.135</td>\n",
       "      <td>-0.028</td>\n",
       "      <td>-0.007</td>\n",
       "      <td>-0.114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          permno       date  ret_1  ret_5  ret_10  ret_20\n",
       "21466675   93436 2021-12-27 -0.005  0.097  -0.033  -0.160\n",
       "21466676   93436 2021-12-28 -0.002  0.056  -0.022  -0.139\n",
       "21466677   93436 2021-12-29 -0.015  0.002   0.018  -0.237\n",
       "21466678   93436 2021-12-30 -0.013 -0.005  -0.036  -0.209\n",
       "21466679   93436 2021-12-31  0.135 -0.028  -0.007  -0.114"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret = pd.read_sas(__depends__[2], encoding = 'latin-1')\n",
    "ret.columns = ret.columns.str.lower()\n",
    "ret['permno'] = ret['permno'].astype(int)\n",
    "ret.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4824d09d",
   "metadata": {
    "id": "2e445687"
   },
   "source": [
    "- ret_i, i = 1,5,10,20, is the return over next day, 5 days, 10 days, 20 days\n",
    "    - I already moved the return backward for one trading day to avoid potentisl forward-looking bias. For example, -0.003 ret_1 of stock 93436 at 2022-06-24 is the return earned by this stock at 2022-06-27"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c58e3de0",
   "metadata": {},
   "source": [
    "# More Data Explorations\n",
    "> In hindsight, we present stock correlation anaysis between imbalance trades and return rates in 5 days, as an example "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3b438b2f",
   "metadata": {
    "id": "7161818f",
    "outputId": "0b7f0df1-96e3-4e9b-a330-089052c427ae"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tic</th>\n",
       "      <th>permno</th>\n",
       "      <th>date</th>\n",
       "      <th>moribvol</th>\n",
       "      <th>russellgroup</th>\n",
       "      <th>sector</th>\n",
       "      <th>price</th>\n",
       "      <th>ret_1</th>\n",
       "      <th>ret_5</th>\n",
       "      <th>ret_10</th>\n",
       "      <th>ret_20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EWST</td>\n",
       "      <td>10001</td>\n",
       "      <td>2007-01-03</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>5</td>\n",
       "      <td>XLU</td>\n",
       "      <td>11.100</td>\n",
       "      <td>0.023</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EWST</td>\n",
       "      <td>10001</td>\n",
       "      <td>2007-01-04</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5</td>\n",
       "      <td>XLU</td>\n",
       "      <td>11.360</td>\n",
       "      <td>-0.010</td>\n",
       "      <td>0.008</td>\n",
       "      <td>-0.018</td>\n",
       "      <td>-0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EWST</td>\n",
       "      <td>10001</td>\n",
       "      <td>2007-01-05</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5</td>\n",
       "      <td>XLU</td>\n",
       "      <td>11.250</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.010</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EWST</td>\n",
       "      <td>10001</td>\n",
       "      <td>2007-01-08</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5</td>\n",
       "      <td>XLU</td>\n",
       "      <td>11.345</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EWST</td>\n",
       "      <td>10001</td>\n",
       "      <td>2007-01-09</td>\n",
       "      <td>1.000</td>\n",
       "      <td>5</td>\n",
       "      <td>XLU</td>\n",
       "      <td>11.240</td>\n",
       "      <td>-0.012</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14431820</th>\n",
       "      <td>TSLA</td>\n",
       "      <td>93436</td>\n",
       "      <td>2021-12-27</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1</td>\n",
       "      <td>XLY</td>\n",
       "      <td>1,093.940</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.097</td>\n",
       "      <td>-0.033</td>\n",
       "      <td>-0.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14431821</th>\n",
       "      <td>TSLA</td>\n",
       "      <td>93436</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>-0.021</td>\n",
       "      <td>1</td>\n",
       "      <td>XLY</td>\n",
       "      <td>1,088.470</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.056</td>\n",
       "      <td>-0.022</td>\n",
       "      <td>-0.139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14431822</th>\n",
       "      <td>TSLA</td>\n",
       "      <td>93436</td>\n",
       "      <td>2021-12-29</td>\n",
       "      <td>-0.033</td>\n",
       "      <td>1</td>\n",
       "      <td>XLY</td>\n",
       "      <td>1,086.190</td>\n",
       "      <td>-0.015</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.018</td>\n",
       "      <td>-0.237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14431823</th>\n",
       "      <td>TSLA</td>\n",
       "      <td>93436</td>\n",
       "      <td>2021-12-30</td>\n",
       "      <td>-0.007</td>\n",
       "      <td>1</td>\n",
       "      <td>XLY</td>\n",
       "      <td>1,070.340</td>\n",
       "      <td>-0.013</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>-0.036</td>\n",
       "      <td>-0.209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14431824</th>\n",
       "      <td>TSLA</td>\n",
       "      <td>93436</td>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>-0.087</td>\n",
       "      <td>1</td>\n",
       "      <td>XLY</td>\n",
       "      <td>1,056.780</td>\n",
       "      <td>0.135</td>\n",
       "      <td>-0.028</td>\n",
       "      <td>-0.007</td>\n",
       "      <td>-0.114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14431825 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           tic  permno       date  moribvol  russellgroup sector     price  \\\n",
       "0         EWST   10001 2007-01-03    -1.000             5    XLU    11.100   \n",
       "1         EWST   10001 2007-01-04     0.000             5    XLU    11.360   \n",
       "2         EWST   10001 2007-01-05     0.000             5    XLU    11.250   \n",
       "3         EWST   10001 2007-01-08     0.000             5    XLU    11.345   \n",
       "4         EWST   10001 2007-01-09     1.000             5    XLU    11.240   \n",
       "...        ...     ...        ...       ...           ...    ...       ...   \n",
       "14431820  TSLA   93436 2021-12-27     0.001             1    XLY 1,093.940   \n",
       "14431821  TSLA   93436 2021-12-28    -0.021             1    XLY 1,088.470   \n",
       "14431822  TSLA   93436 2021-12-29    -0.033             1    XLY 1,086.190   \n",
       "14431823  TSLA   93436 2021-12-30    -0.007             1    XLY 1,070.340   \n",
       "14431824  TSLA   93436 2021-12-31    -0.087             1    XLY 1,056.780   \n",
       "\n",
       "          ret_1  ret_5  ret_10  ret_20  \n",
       "0         0.023 -0.000   0.035   0.027  \n",
       "1        -0.010  0.008  -0.018  -0.014  \n",
       "2         0.008  0.010  -0.004   0.019  \n",
       "3        -0.009 -0.002   0.010   0.004  \n",
       "4        -0.012  0.019   0.019   0.014  \n",
       "...         ...    ...     ...     ...  \n",
       "14431820 -0.005  0.097  -0.033  -0.160  \n",
       "14431821 -0.002  0.056  -0.022  -0.139  \n",
       "14431822 -0.015  0.002   0.018  -0.237  \n",
       "14431823 -0.013 -0.005  -0.036  -0.209  \n",
       "14431824  0.135 -0.028  -0.007  -0.114  \n",
       "\n",
       "[14431825 rows x 11 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All data points - merged retail imblance trades and returns rates\n",
    "retail_ret = pd.merge(retail[['ticker','permno','date','moribvol', \"russellgroup\", \"sector\", \"price\"]], ret, on = ['permno','date'], how = 'inner')\n",
    "retail_ret = retail_ret.rename(columns={'ticker': 'tic'})\n",
    "retail_ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "539e47d1",
   "metadata": {
    "id": "a3baab9c"
   },
   "outputs": [],
   "source": [
    "# Correlation anaysis between imbalance trades and return rates in 5 days\n",
    "fut_ret = 'ret_5'\n",
    "\n",
    "# tic, russellgroup, and sector may be changed with respect to a company over time...\n",
    "grouped = retail_ret.groupby(['permno', 'tic', \"russellgroup\", \"sector\"])\n",
    "\n",
    "df_correlation = grouped.apply(lambda x: x['moribvol'].corr(x[fut_ret])).dropna()\n",
    "df_correlation = df_correlation[df_correlation > 0].reset_index()\n",
    "df_correlation = df_correlation.rename(columns={df_correlation.columns[-1]: 'corr'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "291d016a",
   "metadata": {
    "id": "4a857b79",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_correlation.groupby(['russellgroup','sector'])['permno'].count().unstack().plot.bar();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0cb8c82b",
   "metadata": {
    "id": "4a446d41",
    "outputId": "0f7ac915-17fa-4be2-fc2b-9357160aa372"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x14106f21580>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x3300 with 55 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A panel of plots of correlation values\n",
    "g = sns.FacetGrid(df_correlation, col='russellgroup', row='sector')\n",
    "g.map(sns.histplot, 'corr', kde=False)\n",
    "g.fig.tight_layout()\n",
    "g.fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2aabd8a1",
   "metadata": {
    "id": "837a5e6f",
    "outputId": "1ebeef3f-55d4-4a93-eb5a-539524e9fb30"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sector\n",
       "XLB    AxesSubplot(0.125,0.11;0.775x0.77)\n",
       "XLC    AxesSubplot(0.125,0.11;0.775x0.77)\n",
       "XLE    AxesSubplot(0.125,0.11;0.775x0.77)\n",
       "XLF    AxesSubplot(0.125,0.11;0.775x0.77)\n",
       "XLI    AxesSubplot(0.125,0.11;0.775x0.77)\n",
       "XLK    AxesSubplot(0.125,0.11;0.775x0.77)\n",
       "XLP    AxesSubplot(0.125,0.11;0.775x0.77)\n",
       "XLR    AxesSubplot(0.125,0.11;0.775x0.77)\n",
       "XLU    AxesSubplot(0.125,0.11;0.775x0.77)\n",
       "XLV    AxesSubplot(0.125,0.11;0.775x0.77)\n",
       "XLY    AxesSubplot(0.125,0.11;0.775x0.77)\n",
       "Name: corr, dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14106f03790>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+/u//Pg4ePBgbN26Mm266KU477bSiS4PFLY+o1wdjePBgB0y5NN75NnMo86rWdrrUR8Y72XUIzqRRb0Tesiu2OyeImo0pYe/o2Oo5PMvo4xqNRiyklKJSKUWlVIpTV6+Lvt6po85GD1P3hXDzYUmtFuXIoq+3N4aHh4oup6Ms7+2JoZF6/PDHB2JwyCiicct6K/GG1X2RN0asQ3SUPJWiFHlkWSlSWmytyfHleSMKf3tHVzFFzMxMkwhQjBQRA0f6Y2jQaJlxjXoe9ZF6pErRlXSvRRPK3H333bFt27a4/fbbY926dbF169a4/PLL47HHHotq9dgrZ2FBGP9QMx+jZo75bDQ6fmJWmcGJPkDkeTQarexgH+t8PeEHvBN9ysnj+CNpTvTw2X+QGg8Eyj1Z1FbU5vb9jmu2+9G+0UGzUa6N/dpKKbIsRZ6d4OSe42f6buumH1tqJ1LKolTtiTSnqQDT2P/y6KZ9P5Genp4YGKzHN77zYry4Y8+U+0abhKzjf9qt6Jp6w/rXxPpTVsdrV6yKlT2ubppsWW8tynkWS7NStHkZuI5WS6UoNVKsihSDHf2Kab+elEVKWfTnw9HQYzrFkko5SiOdMw1qpxgPHdLY+xlelaXhGNbpNa1KTy0aud6vowmrZqaNmZ5zZnpC8ellWRaNSNF/pBFHDrswaVx9WT0ajYiyU2bOFsVHzaGhobj//vvj+uuvj7e//e0REXHnnXfGhRdeGI8//ni8853vLLZAaIEUEVlWiqhGR/Q21geHC3vjk1JEqaczPsDM9jiMr+1RHxmM4YGD81zFiUYETbftbLdvrZH60jlMebfw3yWUa6Pnd6mURU+1NxpzDDjTApx6p1yqRKSI/qFGHD5m1EOKLDU64MyeWSsCoxQRI40YDaueeSle2rnnhI9ZTH769Wti1dLl8R9P/7/Y8cq+osvpGD912inxjnPfGN/8zgux45W9RZfTUdb+xKpYu6ovfvjKrth9YL5/Z3ev5Ut74/yfekOMZEKZo2WRR+PQQBweGFlwv3dPRooUPeWI53/w3Rg4crjocjpKbcnSeP2bz4+hBfhe7WTVKqUo13WyT5ZlKQb6D8fIwIFo1+zp3aRSrUWeVZwzk1g7cGZZ5NGzdHmcdvY5MTTYnjWiu0G5UooYW9uWuVkUocz3v//9OHz4cGzevHnitr6+vjjrrLPiySefFMqwMKXR6ZdGhkdO+qrNozsFm+mmTylFpVKOlE3ziXzSVRijI3pa03k+frXH8MiJP/gef0WZVzV7RFNKUSmXR/e5iZ9H3sjn3ME+fR0RzU1tdZKjg6Z9ypP40JQ35jDl3TT7e4JD0Enr6MxGvd4TEfnoVV/DedSHZ3kFTx6Tgq3J44Pat+8pZS3NzfL66L7UltRi5WtWz7RV6wo4Wa0oLUX01HoiUsRwnuLQkaFoFLym1+hMcqNDvlJKkQq8qrN/eHRNpiODI3Gwv7ip3UbXpCv23Jz8s+gf+xB6ZLAeh2ZzXNJYW3qy+5DnHbLm3MxOWd0XlVIpVtX6ojfrLbqcjtFTKUejHvHcvgNxZMCVpZMtq5Xjtb1L4lv//cLEa4uI3p5KbPw/p8ayFSuiXB7vqhh/LzpPuvRandqSpVGPFD/YezCGZvnZoIPf3cysyaKr5SzOWrUksoFDHTAVdudolLLoH0rx4v/7vzE80IHT1M5qto3WfOtKtRqv/+k3RbUx0vZzppNfkynL4nCtFj/YezgGBTNTrKpV4vXLl8ThqMVIuTMu9u0EpSyL4bwU1W5Yz7pDpbzoT3tt8Pjjj8fv/d7vxX/9139FrfbqNEAf/OAHY2BgID71qU81/Zx5nkfDJQdTpBRRb+RxZHB47NgUdXzSpD7kzqihXCpFrVqO/sHhqLftF38nHIej65iqXMrGjstI649LpxyLWXr12AxHXVszoa3nTKvNY9Y1cVyG6mNrwjCuVMqiVilNf86kV78s/HdDU716zox4P3OUiXPGsZmilGVRqxZzXDr948r462lgqO6cmaSUpaj1lMfexzguk6U0OmHo4LCRMpNlKUVPteyYzCClFPVG3lUXDrVaihSlLEXeaDhvJklpdNaMRqO+6N7jnsjolG6Z19FRRj+aJn0P00hjFxoNDJmmdrIspahVy6NtsMMyRZalWU0FuChGyvT390dEHLN2TE9PT+zfv39Oz5lSilKpSy+zaaEsi1hRnstaBotDxbGZluMyM8dmeo7L9ByXmTk203NcZubYTM9xmZljMz2HZWaVsrVNaY7X0wxKLtWeTslxoUnamJlVyqbqYn4tihZ6fHTM0NDUYZuDg4PR22uKAQAAAAAAoPUWRShz6qmnRkTErl27pty+a9euWLt2bRElAQAAAAAAi8yiCGXe9KY3xbJly+KJJ56YuO3AgQPx7LPPxsaNGwusDAAAAAAAWCwWxZoy1Wo1Lr300rjjjjti9erV8brXvS62bt0a69ati4suuqjo8gAAAAAAgEVgUYQyERFXXXVVjIyMxI033hgDAwOxcePGuO+++6JSqRRdGgAAAAAAsAikPM/zoosAAAAAAABY6BbFmjIAAAAAAABFE8oAAAAAAAC0gVAGAAAAAACgDYQyAAAAAAAAbSCUAQAAAAAAaAOhDAAAAAAAQBsIZZi1RqMRH//4x+PCCy+Mc845J6644op4/vnnZ9x+7969cd1118XGjRtj06ZN8eEPfzj6+/vbWDHQbZptZ37wgx/Eb/3Wb8Vb3/rW2Lx5c1x11VXx0ksvtbFioNs0285M9rnPfS7OPPPMeOGFF1pcJdDNmm1nhoeH46Mf/ejE9pdeeml873vfa2PFQDdpto3ZvXt3XHfddfG2t70t3vrWt8Y111wTO3fubGPFQDf71Kc+FZdddtlxt9EH3DyhDLN29913x7Zt2+KWW26Jv/3bv41GoxGXX355DA0NTbv9VVddFdu3b4+//uu/jj//8z+Pf//3f4+bb765vUUDXaWZdmbv3r3xvve9L2q1WnzmM5+Jv/zLv4w9e/bE5ZdfHoODgwVUD3SDZt/PjHvxxRfjIx/5SJuqBLpZs+3MzTffHI888kjcdttt8fDDD8fq1avjiiuuiIMHD7a5cqAbNNvGXH311fHSSy/Fpz/96fj0pz8dL730Uvzu7/5um6sGutFDDz0Ud9111wm30wc8BznMwuDgYL5hw4b8oYcemrht//79+Vve8pb8scceO2b7b33rW/kZZ5yR/8///M/EbV/96lfzM888M9+xY0dbaga6S7PtzN/93d/lGzZsyPv7+ydue+mll/Izzjgj//rXv96WmoHu0mw7M65er+eXXHJJ/uu//uv5GWeckT///PPtKBfoQs22M//7v/+bn3nmmfm//du/Tdn+53/+572fAY7RbBuzf//+/Iwzzsj/5V/+ZeK2r3zlK/kZZ5yR7927tx0lA11ox44d+ZVXXpmfc845+cUXX5xfeumlM26rD3hujJRhVr7//e/H4cOHY/PmzRO39fX1xVlnnRVPPvnkMds/9dRTccopp8Qb3/jGids2bdoUKaV4+umn21Iz0F2abWc2b94cd999d9RqtYnbsmz019qBAwdaXzDQdZptZ8bdc889MTw8HFdeeWU7ygS6WLPtzNe+9rVYvnx5bNmyZcr2//qv/zrlOQAimm9jarVaLF26NB599NE4dOhQHDp0KP7xH/8xTj/99Ojr62tn6UAX+e53vxuVSiU+97nPxdlnn33cbfUBz0256ALoDjt27IiIiFNPPXXK7WvWrJm4b7KdO3ces221Wo2VK1fGyy+/3LpCga7VbDuzfv36WL9+/ZTb7r333qjVarFx48bWFQp0rWbbmYiIZ555Ju6///747Gc/a/514ISabWeee+65OO200+Lxxx+Pe++9N3bu3BlnnXVW/OEf/uGUzg2AiObbmGq1GrfffnvcdNNNcf7550dKKdasWRMPPvjgxAVtAEd7xzveEe94xztmta0+4LnRAjMr44szVavVKbf39PRMu3ZDf3//Mdseb3uAZtuZo33mM5+JBx98MK6//vpYvXp1S2oEuluz7cyRI0fi+uuvj+uvvz5+8id/sh0lAl2u2Xbm0KFDsX379rj77rvj2muvjb/4i7+Icrkcv/Zrvxa7d+9uS81A92i2jcnzPL73ve/Fhg0b4qGHHooHHnggXvva18bv/M7vxKFDh9pSM7Cw6QOeG6EMszI+PdDRC8cNDg5Gb2/vtNtPt8jc4OBgLFmypDVFAl2t2XZmXJ7ncdddd8Wtt94av/3bvx2XXXZZS+sEulez7cytt94ap59+evzqr/5qW+oDul+z7Uy5XI5Dhw7FnXfeGRdccEG85S1viTvvvDMiIv7hH/6h9QUDXaXZNuaf//mf48EHH4ytW7fGeeedF5s2bYp77rknXnzxxfjsZz/blpqBhU0f8NwIZZiV8WFou3btmnL7rl27Yu3atcdsv27dumO2HRoain379sWaNWtaVyjQtZptZyIihoeH4/d///fjnnvuiQ996ENx9dVXt7pMoIs12848/PDD8fWvfz02bNgQGzZsiCuuuCIiIt75znfGPffc0/qCga4zl89N5XJ5ylRltVotTjvttHjhhRdaWyzQdZptY5566qk4/fTTY9myZRO3rVixIk4//fTYvn17a4sFFgV9wHMjlGFW3vSmN8WyZcviiSeemLjtwIED8eyzz067dsPGjRtjx44dU37Jf/Ob34yIiPPOO6/1BQNdp9l2JiLihhtuiC9+8Yvx0Y9+NH7jN36jTZUC3arZdubxxx+Pz3/+8/Hoo4/Go48+GrfeemtEjK5fZfQMMJ25fG4aGRmJ73znOxO3DQwMxPPPPx9veMMb2lIz0D2abWPWrVsX27dvnzKF0JEjR+KFF14wNSswL/QBz0256ALoDtVqNS699NK44447YvXq1fG6170utm7dGuvWrYuLLroo6vV67NmzJ5YvXx61Wi3OPvvsOPfcc+Oaa66Jm2++OY4cORI33XRTvOtd75rxindgcWu2nXnkkUfiC1/4Qtxwww2xadOmeOWVVyaea3wbgMmabWeO7hAdX0D3ta99baxcubKAPQA6XbPtzPnnnx8/+7M/G3/wB38QH/nIR2LlypXx8Y9/PEqlUvzSL/1S0bsDdJhm25h3vetdcd9998XVV18dH/zgByMi4q677oqenp5497vfXfDeAN1IH/D8MFKGWbvqqqviPe95T9x4441xySWXRKlUivvuuy8qlUq8/PLLccEFF8QXvvCFiIhIKcUnP/nJWL9+fbz3ve+Nq6++OrZs2RI333xzsTsBdLRm2pnPf/7zERHxZ3/2Z3HBBRdM+TO+DcDRmmlnAOai2XbmE5/4RGzatCk+8IEPxHve8544dOhQ/M3f/E2sXr26wL0AOlUzbcyaNWti27Ztked5vPe97433ve99UalUYtu2bbF8+fKC9wToRvqA50fK8zwvuggAAAAAAICFzkgZAAAAAACANhDKAAAAAAAAtIFQBgAAAAAAoA2EMgAAAAAAAG0glAEAAAAAAGgDoQwAAAAAAEAbCGUAAAAAAADaQCgDAAAAAADQBkIZAAAAAACANhDKAAAAAAAAtIFQBgAAAAAAoA2EMgAAAAAAAG3w/wHD2bj3dwOwsgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution of correlation values for each sectors\n",
    "grouped = df_correlation.groupby(['sector'])\n",
    "grouped['corr'].plot.hist(alpha=0.5, bins=20)\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8fa3d748",
   "metadata": {
    "id": "bf504528",
    "outputId": "fec50905-d47a-49bc-b437-c3809850847d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>permno</th>\n",
       "      <th>tic</th>\n",
       "      <th>russellgroup</th>\n",
       "      <th>sector</th>\n",
       "      <th>corr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>russellgroup</th>\n",
       "      <th>sector</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">1</th>\n",
       "      <th>XLB</th>\n",
       "      <td>16851</td>\n",
       "      <td>DWDP</td>\n",
       "      <td>1</td>\n",
       "      <td>XLB</td>\n",
       "      <td>0.229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLC</th>\n",
       "      <td>91066</td>\n",
       "      <td>VIA</td>\n",
       "      <td>1</td>\n",
       "      <td>XLC</td>\n",
       "      <td>0.121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLE</th>\n",
       "      <td>13141</td>\n",
       "      <td>WPX</td>\n",
       "      <td>1</td>\n",
       "      <td>XLE</td>\n",
       "      <td>0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLF</th>\n",
       "      <td>64565</td>\n",
       "      <td>CFC</td>\n",
       "      <td>1</td>\n",
       "      <td>XLF</td>\n",
       "      <td>0.134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLI</th>\n",
       "      <td>11955</td>\n",
       "      <td>WMI</td>\n",
       "      <td>1</td>\n",
       "      <td>XLI</td>\n",
       "      <td>0.162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLK</th>\n",
       "      <td>14926</td>\n",
       "      <td>CDK</td>\n",
       "      <td>1</td>\n",
       "      <td>XLK</td>\n",
       "      <td>0.256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLP</th>\n",
       "      <td>18163</td>\n",
       "      <td>PG</td>\n",
       "      <td>1</td>\n",
       "      <td>XLP</td>\n",
       "      <td>0.073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLR</th>\n",
       "      <td>91388</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>XLR</td>\n",
       "      <td>0.140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLU</th>\n",
       "      <td>24563</td>\n",
       "      <td>TXU</td>\n",
       "      <td>1</td>\n",
       "      <td>XLU</td>\n",
       "      <td>0.142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLV</th>\n",
       "      <td>16544</td>\n",
       "      <td>BIVV</td>\n",
       "      <td>1</td>\n",
       "      <td>XLV</td>\n",
       "      <td>0.244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLY</th>\n",
       "      <td>66181</td>\n",
       "      <td>HD</td>\n",
       "      <td>1</td>\n",
       "      <td>XLY</td>\n",
       "      <td>0.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">2</th>\n",
       "      <th>XLB</th>\n",
       "      <td>15917</td>\n",
       "      <td>GCP</td>\n",
       "      <td>2</td>\n",
       "      <td>XLB</td>\n",
       "      <td>0.184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLC</th>\n",
       "      <td>79903</td>\n",
       "      <td>HHS</td>\n",
       "      <td>2</td>\n",
       "      <td>XLC</td>\n",
       "      <td>0.114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLE</th>\n",
       "      <td>90180</td>\n",
       "      <td>ATLS</td>\n",
       "      <td>2</td>\n",
       "      <td>XLE</td>\n",
       "      <td>0.139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLF</th>\n",
       "      <td>81593</td>\n",
       "      <td>WM</td>\n",
       "      <td>2</td>\n",
       "      <td>XLF</td>\n",
       "      <td>0.326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLI</th>\n",
       "      <td>81285</td>\n",
       "      <td>TT</td>\n",
       "      <td>2</td>\n",
       "      <td>XLI</td>\n",
       "      <td>0.191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLK</th>\n",
       "      <td>81705</td>\n",
       "      <td>FEIC</td>\n",
       "      <td>2</td>\n",
       "      <td>XLK</td>\n",
       "      <td>0.267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLP</th>\n",
       "      <td>52038</td>\n",
       "      <td>SYY</td>\n",
       "      <td>2</td>\n",
       "      <td>XLP</td>\n",
       "      <td>0.145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLR</th>\n",
       "      <td>14252</td>\n",
       "      <td>GLPI</td>\n",
       "      <td>2</td>\n",
       "      <td>XLR</td>\n",
       "      <td>0.304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLU</th>\n",
       "      <td>14401</td>\n",
       "      <td>OGS</td>\n",
       "      <td>2</td>\n",
       "      <td>XLU</td>\n",
       "      <td>0.222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLV</th>\n",
       "      <td>86923</td>\n",
       "      <td>TRI</td>\n",
       "      <td>2</td>\n",
       "      <td>XLV</td>\n",
       "      <td>0.216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLY</th>\n",
       "      <td>18578</td>\n",
       "      <td>KTB</td>\n",
       "      <td>2</td>\n",
       "      <td>XLY</td>\n",
       "      <td>0.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">3</th>\n",
       "      <th>XLB</th>\n",
       "      <td>18075</td>\n",
       "      <td>AP</td>\n",
       "      <td>3</td>\n",
       "      <td>XLB</td>\n",
       "      <td>0.231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLC</th>\n",
       "      <td>92265</td>\n",
       "      <td>RCH</td>\n",
       "      <td>3</td>\n",
       "      <td>XLC</td>\n",
       "      <td>0.806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLE</th>\n",
       "      <td>90674</td>\n",
       "      <td>AEZ</td>\n",
       "      <td>3</td>\n",
       "      <td>XLE</td>\n",
       "      <td>0.348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLF</th>\n",
       "      <td>15180</td>\n",
       "      <td>ICBK</td>\n",
       "      <td>3</td>\n",
       "      <td>XLF</td>\n",
       "      <td>0.666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLI</th>\n",
       "      <td>91299</td>\n",
       "      <td>GFN</td>\n",
       "      <td>3</td>\n",
       "      <td>XLI</td>\n",
       "      <td>0.437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLK</th>\n",
       "      <td>80236</td>\n",
       "      <td>DSPG</td>\n",
       "      <td>3</td>\n",
       "      <td>XLK</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLP</th>\n",
       "      <td>76461</td>\n",
       "      <td>VERU</td>\n",
       "      <td>3</td>\n",
       "      <td>XLP</td>\n",
       "      <td>0.282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLR</th>\n",
       "      <td>38746</td>\n",
       "      <td>AXR</td>\n",
       "      <td>3</td>\n",
       "      <td>XLR</td>\n",
       "      <td>0.412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLU</th>\n",
       "      <td>91840</td>\n",
       "      <td>RZ</td>\n",
       "      <td>3</td>\n",
       "      <td>XLU</td>\n",
       "      <td>0.276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLV</th>\n",
       "      <td>16670</td>\n",
       "      <td>CATS</td>\n",
       "      <td>3</td>\n",
       "      <td>XLV</td>\n",
       "      <td>0.746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLY</th>\n",
       "      <td>87379</td>\n",
       "      <td>CLCT</td>\n",
       "      <td>3</td>\n",
       "      <td>XLY</td>\n",
       "      <td>0.790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">4</th>\n",
       "      <th>XLB</th>\n",
       "      <td>10881</td>\n",
       "      <td>ANLT</td>\n",
       "      <td>4</td>\n",
       "      <td>XLB</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLC</th>\n",
       "      <td>65787</td>\n",
       "      <td>TRB</td>\n",
       "      <td>4</td>\n",
       "      <td>XLC</td>\n",
       "      <td>0.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLE</th>\n",
       "      <td>83991</td>\n",
       "      <td>THX</td>\n",
       "      <td>4</td>\n",
       "      <td>XLE</td>\n",
       "      <td>0.867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLF</th>\n",
       "      <td>86300</td>\n",
       "      <td>PCBS</td>\n",
       "      <td>4</td>\n",
       "      <td>XLF</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLI</th>\n",
       "      <td>21020</td>\n",
       "      <td>AMR</td>\n",
       "      <td>4</td>\n",
       "      <td>XLI</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLK</th>\n",
       "      <td>15797</td>\n",
       "      <td>CSRA</td>\n",
       "      <td>4</td>\n",
       "      <td>XLK</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLP</th>\n",
       "      <td>13598</td>\n",
       "      <td>KRFT</td>\n",
       "      <td>4</td>\n",
       "      <td>XLP</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLR</th>\n",
       "      <td>91388</td>\n",
       "      <td>H</td>\n",
       "      <td>4</td>\n",
       "      <td>XLR</td>\n",
       "      <td>0.470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLU</th>\n",
       "      <td>77495</td>\n",
       "      <td>MWAV</td>\n",
       "      <td>4</td>\n",
       "      <td>XLU</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLV</th>\n",
       "      <td>66368</td>\n",
       "      <td>CTT</td>\n",
       "      <td>4</td>\n",
       "      <td>XLV</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLY</th>\n",
       "      <td>11873</td>\n",
       "      <td>DISK</td>\n",
       "      <td>4</td>\n",
       "      <td>XLY</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">5</th>\n",
       "      <th>XLB</th>\n",
       "      <td>91946</td>\n",
       "      <td>SHJ</td>\n",
       "      <td>5</td>\n",
       "      <td>XLB</td>\n",
       "      <td>0.385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLC</th>\n",
       "      <td>17323</td>\n",
       "      <td>LIVX</td>\n",
       "      <td>5</td>\n",
       "      <td>XLC</td>\n",
       "      <td>0.833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLE</th>\n",
       "      <td>79393</td>\n",
       "      <td>EWEB</td>\n",
       "      <td>5</td>\n",
       "      <td>XLE</td>\n",
       "      <td>0.871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLF</th>\n",
       "      <td>10682</td>\n",
       "      <td>UBMT</td>\n",
       "      <td>5</td>\n",
       "      <td>XLF</td>\n",
       "      <td>0.412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLI</th>\n",
       "      <td>18533</td>\n",
       "      <td>RMO</td>\n",
       "      <td>5</td>\n",
       "      <td>XLI</td>\n",
       "      <td>0.607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLK</th>\n",
       "      <td>15410</td>\n",
       "      <td>APPF</td>\n",
       "      <td>5</td>\n",
       "      <td>XLK</td>\n",
       "      <td>0.753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLP</th>\n",
       "      <td>89447</td>\n",
       "      <td>CVGW</td>\n",
       "      <td>5</td>\n",
       "      <td>XLP</td>\n",
       "      <td>0.234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLR</th>\n",
       "      <td>20147</td>\n",
       "      <td>HFEN</td>\n",
       "      <td>5</td>\n",
       "      <td>XLR</td>\n",
       "      <td>0.294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLU</th>\n",
       "      <td>84345</td>\n",
       "      <td>USEY</td>\n",
       "      <td>5</td>\n",
       "      <td>XLU</td>\n",
       "      <td>0.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLV</th>\n",
       "      <td>19397</td>\n",
       "      <td>TOI</td>\n",
       "      <td>5</td>\n",
       "      <td>XLV</td>\n",
       "      <td>0.479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLY</th>\n",
       "      <td>18936</td>\n",
       "      <td>XL</td>\n",
       "      <td>5</td>\n",
       "      <td>XLY</td>\n",
       "      <td>0.519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     permno   tic  russellgroup sector  corr\n",
       "russellgroup sector                                         \n",
       "1            XLB      16851  DWDP             1    XLB 0.229\n",
       "             XLC      91066   VIA             1    XLC 0.121\n",
       "             XLE      13141   WPX             1    XLE 0.118\n",
       "             XLF      64565   CFC             1    XLF 0.134\n",
       "             XLI      11955   WMI             1    XLI 0.162\n",
       "             XLK      14926   CDK             1    XLK 0.256\n",
       "             XLP      18163    PG             1    XLP 0.073\n",
       "             XLR      91388     H             1    XLR 0.140\n",
       "             XLU      24563   TXU             1    XLU 0.142\n",
       "             XLV      16544  BIVV             1    XLV 0.244\n",
       "             XLY      66181    HD             1    XLY 0.061\n",
       "2            XLB      15917   GCP             2    XLB 0.184\n",
       "             XLC      79903   HHS             2    XLC 0.114\n",
       "             XLE      90180  ATLS             2    XLE 0.139\n",
       "             XLF      81593    WM             2    XLF 0.326\n",
       "             XLI      81285    TT             2    XLI 0.191\n",
       "             XLK      81705  FEIC             2    XLK 0.267\n",
       "             XLP      52038   SYY             2    XLP 0.145\n",
       "             XLR      14252  GLPI             2    XLR 0.304\n",
       "             XLU      14401   OGS             2    XLU 0.222\n",
       "             XLV      86923   TRI             2    XLV 0.216\n",
       "             XLY      18578   KTB             2    XLY 0.375\n",
       "3            XLB      18075    AP             3    XLB 0.231\n",
       "             XLC      92265   RCH             3    XLC 0.806\n",
       "             XLE      90674   AEZ             3    XLE 0.348\n",
       "             XLF      15180  ICBK             3    XLF 0.666\n",
       "             XLI      91299   GFN             3    XLI 0.437\n",
       "             XLK      80236  DSPG             3    XLK 1.000\n",
       "             XLP      76461  VERU             3    XLP 0.282\n",
       "             XLR      38746   AXR             3    XLR 0.412\n",
       "             XLU      91840    RZ             3    XLU 0.276\n",
       "             XLV      16670  CATS             3    XLV 0.746\n",
       "             XLY      87379  CLCT             3    XLY 0.790\n",
       "4            XLB      10881  ANLT             4    XLB 1.000\n",
       "             XLC      65787   TRB             4    XLC 0.682\n",
       "             XLE      83991   THX             4    XLE 0.867\n",
       "             XLF      86300  PCBS             4    XLF 1.000\n",
       "             XLI      21020   AMR             4    XLI 1.000\n",
       "             XLK      15797  CSRA             4    XLK 1.000\n",
       "             XLP      13598  KRFT             4    XLP 1.000\n",
       "             XLR      91388     H             4    XLR 0.470\n",
       "             XLU      77495  MWAV             4    XLU 1.000\n",
       "             XLV      66368   CTT             4    XLV 1.000\n",
       "             XLY      11873  DISK             4    XLY 1.000\n",
       "5            XLB      91946   SHJ             5    XLB 0.385\n",
       "             XLC      17323  LIVX             5    XLC 0.833\n",
       "             XLE      79393  EWEB             5    XLE 0.871\n",
       "             XLF      10682  UBMT             5    XLF 0.412\n",
       "             XLI      18533   RMO             5    XLI 0.607\n",
       "             XLK      15410  APPF             5    XLK 0.753\n",
       "             XLP      89447  CVGW             5    XLP 0.234\n",
       "             XLR      20147  HFEN             5    XLR 0.294\n",
       "             XLU      84345  USEY             5    XLU 0.150\n",
       "             XLV      19397   TOI             5    XLV 0.479\n",
       "             XLY      18936    XL             5    XLY 0.519"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Max correlated stocks in different cap sizes and sectors\n",
    "\n",
    "def get_max_row(group):\n",
    "    return group[group['corr'] == group['corr'].max()].iloc[0, :]\n",
    "\n",
    "df_correlation.groupby(['russellgroup','sector']).apply(get_max_row)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cccf49e1",
   "metadata": {
    "id": "0f76dfbd"
   },
   "source": [
    "# Stock Selection\n",
    "\n",
    "> Pick 11 Large Cap Tech (XLK) firms whose retail investor trades are significantly correlatede with return rates in 5 days.\n",
    "> Stocks are [\"QCOM\", \"ADSK\", \"FSLR\", \"MSFT\", \"AMD\", \"ORCL\", \"INTU\", \"WU\", \"LRCX\", \"TXN\", \"CSCO\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fcc74c64",
   "metadata": {
    "id": "4f440471",
    "outputId": "93ab91bf-d158-4aea-aa0d-bdc679f7d853"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticker</th>\n",
       "      <th>permno</th>\n",
       "      <th>date</th>\n",
       "      <th>moribvol</th>\n",
       "      <th>price</th>\n",
       "      <th>russellgroup</th>\n",
       "      <th>ret_1</th>\n",
       "      <th>ret_5</th>\n",
       "      <th>ret_10</th>\n",
       "      <th>ret_20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SUNW</td>\n",
       "      <td>10078</td>\n",
       "      <td>2007-01-03</td>\n",
       "      <td>-0.161</td>\n",
       "      <td>5.540</td>\n",
       "      <td>1</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SUNW</td>\n",
       "      <td>10078</td>\n",
       "      <td>2007-01-04</td>\n",
       "      <td>0.236</td>\n",
       "      <td>5.690</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.016</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SUNW</td>\n",
       "      <td>10078</td>\n",
       "      <td>2007-01-05</td>\n",
       "      <td>0.254</td>\n",
       "      <td>5.600</td>\n",
       "      <td>1</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SUNW</td>\n",
       "      <td>10078</td>\n",
       "      <td>2007-01-08</td>\n",
       "      <td>-0.294</td>\n",
       "      <td>5.650</td>\n",
       "      <td>1</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SUNW</td>\n",
       "      <td>10078</td>\n",
       "      <td>2007-01-09</td>\n",
       "      <td>0.762</td>\n",
       "      <td>5.730</td>\n",
       "      <td>1</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88785</th>\n",
       "      <td>AOL</td>\n",
       "      <td>93091</td>\n",
       "      <td>2010-05-24</td>\n",
       "      <td>-0.040</td>\n",
       "      <td>21.200</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.025</td>\n",
       "      <td>-0.033</td>\n",
       "      <td>-0.041</td>\n",
       "      <td>0.082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88786</th>\n",
       "      <td>AOL</td>\n",
       "      <td>93091</td>\n",
       "      <td>2010-05-25</td>\n",
       "      <td>0.038</td>\n",
       "      <td>20.680</td>\n",
       "      <td>1</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.015</td>\n",
       "      <td>-0.023</td>\n",
       "      <td>0.095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88787</th>\n",
       "      <td>AOL</td>\n",
       "      <td>93091</td>\n",
       "      <td>2010-05-26</td>\n",
       "      <td>-0.280</td>\n",
       "      <td>20.720</td>\n",
       "      <td>1</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88788</th>\n",
       "      <td>AOL</td>\n",
       "      <td>93091</td>\n",
       "      <td>2010-05-27</td>\n",
       "      <td>0.159</td>\n",
       "      <td>21.160</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.025</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-0.008</td>\n",
       "      <td>0.038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88789</th>\n",
       "      <td>AOL</td>\n",
       "      <td>93091</td>\n",
       "      <td>2010-05-28</td>\n",
       "      <td>0.665</td>\n",
       "      <td>20.630</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.007</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.056</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88790 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ticker  permno       date  moribvol  price  russellgroup  ret_1  ret_5  \\\n",
       "0       SUNW   10078 2007-01-03    -0.161  5.540             1  0.027  0.083   \n",
       "1       SUNW   10078 2007-01-04     0.236  5.690             1 -0.016  0.079   \n",
       "2       SUNW   10078 2007-01-05     0.254  5.600             1  0.009  0.082   \n",
       "3       SUNW   10078 2007-01-08    -0.294  5.650             1  0.014  0.073   \n",
       "4       SUNW   10078 2007-01-09     0.762  5.730             1  0.047  0.045   \n",
       "...      ...     ...        ...       ...    ...           ...    ...    ...   \n",
       "88785    AOL   93091 2010-05-24    -0.040 21.200             1 -0.025 -0.033   \n",
       "88786    AOL   93091 2010-05-25     0.038 20.680             1  0.002  0.015   \n",
       "88787    AOL   93091 2010-05-26    -0.280 20.720             1  0.021  0.014   \n",
       "88788    AOL   93091 2010-05-27     0.159 21.160             1 -0.025  0.006   \n",
       "88789    AOL   93091 2010-05-28     0.665 20.630             1 -0.007 -0.001   \n",
       "\n",
       "       ret_10  ret_20  \n",
       "0       0.045   0.182  \n",
       "1       0.014   0.165  \n",
       "2       0.027   0.186  \n",
       "3       0.002   0.156  \n",
       "4       0.073   0.150  \n",
       "...       ...     ...  \n",
       "88785  -0.041   0.082  \n",
       "88786  -0.023   0.095  \n",
       "88787   0.019   0.049  \n",
       "88788  -0.008   0.038  \n",
       "88789   0.006   0.056  \n",
       "\n",
       "[88790 rows x 10 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# stock_grp = ['Large-Cap','Mid-Cap','Small-Cap','Micro-Cap','Nano-Cap']\n",
    "# tech_largeCap = retail[(retail[\"sector\"] == 'XLK')]\n",
    "\n",
    "tech_largeCap = retail[(retail[\"sector\"] == 'XLK')& (retail[\"russellgroup\"] == 1)]\n",
    "tech_largeCap_ret = pd.merge(tech_largeCap[['ticker','permno','date','moribvol', \"price\", \"russellgroup\"]], \n",
    "                             ret, \n",
    "                             on = ['permno','date'], \n",
    "                             how = 'inner')\n",
    "\n",
    "tech_largeCap_ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e434e25e",
   "metadata": {
    "id": "9c1c2667",
    "outputId": "e9baed45-2796-47fb-b3b1-ddb14cfd837f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['SUNW', 'JAVA', 'ORCL', 'MSFT', 'EMC', 'FISV', 'DELL', 'MMI',\n",
       "       'IBM', 'NOW', 'WDAY', 'AAPL', 'AMAT', 'CDK', 'PYPL', 'TXN', 'HPE',\n",
       "       'SQ', 'VSM', 'ZM', 'GLW', 'MOT', 'MSI', 'HPQ', 'DXC', 'ADP',\n",
       "       'LRCX', 'MU', 'INTC', 'ADI', 'AMD', 'ADBE', 'SYMC', 'CSCO', 'QCOM',\n",
       "       'FDC', 'INTU', 'NTAP', 'ADSK', 'BRCM', 'CTSH', 'NVDA', 'JNPR',\n",
       "       'GPN', 'FIS', 'CRM', 'MA', 'WU', 'FSLR', 'VMW', 'V', 'AVGO', 'AOL'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tech_largeCap_ret['ticker'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8941e21d",
   "metadata": {
    "id": "683f9377",
    "outputId": "d99ed301-4a87-485d-bb7a-e8f11dfb0db2"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>permno</th>\n",
       "      <th>corr</th>\n",
       "      <th>ticker</th>\n",
       "      <th>russellgroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14926</td>\n",
       "      <td>0.256</td>\n",
       "      <td>CDK</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>40125</td>\n",
       "      <td>0.105</td>\n",
       "      <td>DXC</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>18484</td>\n",
       "      <td>0.082</td>\n",
       "      <td>ZM</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>77546</td>\n",
       "      <td>0.073</td>\n",
       "      <td>FDC</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>77178</td>\n",
       "      <td>0.055</td>\n",
       "      <td>QCOM</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4512</th>\n",
       "      <td>85631</td>\n",
       "      <td>0.052</td>\n",
       "      <td>ADSK</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5145</th>\n",
       "      <td>91611</td>\n",
       "      <td>0.051</td>\n",
       "      <td>FSLR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5648</th>\n",
       "      <td>10078</td>\n",
       "      <td>0.045</td>\n",
       "      <td>SUNW</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5811</th>\n",
       "      <td>10078</td>\n",
       "      <td>0.045</td>\n",
       "      <td>JAVA</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6003</th>\n",
       "      <td>10107</td>\n",
       "      <td>0.044</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9780</th>\n",
       "      <td>61241</td>\n",
       "      <td>0.041</td>\n",
       "      <td>AMD</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10285</th>\n",
       "      <td>10104</td>\n",
       "      <td>0.039</td>\n",
       "      <td>ORCL</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14062</th>\n",
       "      <td>92257</td>\n",
       "      <td>0.034</td>\n",
       "      <td>VMW</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17652</th>\n",
       "      <td>78975</td>\n",
       "      <td>0.030</td>\n",
       "      <td>INTU</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19060</th>\n",
       "      <td>91461</td>\n",
       "      <td>0.021</td>\n",
       "      <td>WU</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19918</th>\n",
       "      <td>48486</td>\n",
       "      <td>0.017</td>\n",
       "      <td>LRCX</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20320</th>\n",
       "      <td>15579</td>\n",
       "      <td>0.016</td>\n",
       "      <td>TXN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24097</th>\n",
       "      <td>76076</td>\n",
       "      <td>0.016</td>\n",
       "      <td>CSCO</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27874</th>\n",
       "      <td>10147</td>\n",
       "      <td>0.013</td>\n",
       "      <td>EMC</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30308</th>\n",
       "      <td>12490</td>\n",
       "      <td>0.013</td>\n",
       "      <td>IBM</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34085</th>\n",
       "      <td>89003</td>\n",
       "      <td>0.010</td>\n",
       "      <td>FIS</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34718</th>\n",
       "      <td>44644</td>\n",
       "      <td>0.009</td>\n",
       "      <td>ADP</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38495</th>\n",
       "      <td>15826</td>\n",
       "      <td>0.009</td>\n",
       "      <td>SQ</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38897</th>\n",
       "      <td>27828</td>\n",
       "      <td>0.008</td>\n",
       "      <td>HPQ</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42272</th>\n",
       "      <td>14702</td>\n",
       "      <td>0.006</td>\n",
       "      <td>AMAT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45044</th>\n",
       "      <td>82598</td>\n",
       "      <td>0.005</td>\n",
       "      <td>NTAP</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45297</th>\n",
       "      <td>11081</td>\n",
       "      <td>0.004</td>\n",
       "      <td>DELL</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46995</th>\n",
       "      <td>75510</td>\n",
       "      <td>0.004</td>\n",
       "      <td>ADBE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50772</th>\n",
       "      <td>60871</td>\n",
       "      <td>0.003</td>\n",
       "      <td>ADI</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51405</th>\n",
       "      <td>75607</td>\n",
       "      <td>0.002</td>\n",
       "      <td>SYMC</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52769</th>\n",
       "      <td>86158</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>CTSH</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55688</th>\n",
       "      <td>93002</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>AVGO</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56632</th>\n",
       "      <td>86580</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>NVDA</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57788</th>\n",
       "      <td>59328</td>\n",
       "      <td>-0.012</td>\n",
       "      <td>INTC</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61565</th>\n",
       "      <td>10696</td>\n",
       "      <td>-0.012</td>\n",
       "      <td>FISV</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61967</th>\n",
       "      <td>92611</td>\n",
       "      <td>-0.013</td>\n",
       "      <td>V</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65389</th>\n",
       "      <td>85963</td>\n",
       "      <td>-0.013</td>\n",
       "      <td>BRCM</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66920</th>\n",
       "      <td>15488</td>\n",
       "      <td>-0.014</td>\n",
       "      <td>PYPL</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68516</th>\n",
       "      <td>53613</td>\n",
       "      <td>-0.017</td>\n",
       "      <td>MU</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70176</th>\n",
       "      <td>86979</td>\n",
       "      <td>-0.018</td>\n",
       "      <td>JNPR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70934</th>\n",
       "      <td>14593</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74711</th>\n",
       "      <td>90215</td>\n",
       "      <td>-0.022</td>\n",
       "      <td>CRM</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77377</th>\n",
       "      <td>12456</td>\n",
       "      <td>-0.025</td>\n",
       "      <td>MMI</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77441</th>\n",
       "      <td>15707</td>\n",
       "      <td>-0.027</td>\n",
       "      <td>HPE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78319</th>\n",
       "      <td>16279</td>\n",
       "      <td>-0.028</td>\n",
       "      <td>VSM</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78443</th>\n",
       "      <td>91233</td>\n",
       "      <td>-0.030</td>\n",
       "      <td>MA</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81865</th>\n",
       "      <td>22779</td>\n",
       "      <td>-0.039</td>\n",
       "      <td>MOT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82874</th>\n",
       "      <td>22779</td>\n",
       "      <td>-0.039</td>\n",
       "      <td>MSI</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83499</th>\n",
       "      <td>13447</td>\n",
       "      <td>-0.054</td>\n",
       "      <td>NOW</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84132</th>\n",
       "      <td>22293</td>\n",
       "      <td>-0.055</td>\n",
       "      <td>GLW</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87005</th>\n",
       "      <td>13628</td>\n",
       "      <td>-0.067</td>\n",
       "      <td>WDAY</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87638</th>\n",
       "      <td>88873</td>\n",
       "      <td>-0.094</td>\n",
       "      <td>GPN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88040</th>\n",
       "      <td>93091</td>\n",
       "      <td>-0.144</td>\n",
       "      <td>AOL</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       permno   corr ticker  russellgroup\n",
       "0       14926  0.256    CDK             1\n",
       "124     40125  0.105    DXC             1\n",
       "165     18484  0.082     ZM             1\n",
       "567     77546  0.073    FDC             1\n",
       "735     77178  0.055   QCOM             1\n",
       "4512    85631  0.052   ADSK             1\n",
       "5145    91611  0.051   FSLR             1\n",
       "5648    10078  0.045   SUNW             1\n",
       "5811    10078  0.045   JAVA             1\n",
       "6003    10107  0.044   MSFT             1\n",
       "9780    61241  0.041    AMD             1\n",
       "10285   10104  0.039   ORCL             1\n",
       "14062   92257  0.034    VMW             1\n",
       "17652   78975  0.030   INTU             1\n",
       "19060   91461  0.021     WU             1\n",
       "19918   48486  0.017   LRCX             1\n",
       "20320   15579  0.016    TXN             1\n",
       "24097   76076  0.016   CSCO             1\n",
       "27874   10147  0.013    EMC             1\n",
       "30308   12490  0.013    IBM             1\n",
       "34085   89003  0.010    FIS             1\n",
       "34718   44644  0.009    ADP             1\n",
       "38495   15826  0.009     SQ             1\n",
       "38897   27828  0.008    HPQ             1\n",
       "42272   14702  0.006   AMAT             1\n",
       "45044   82598  0.005   NTAP             1\n",
       "45297   11081  0.004   DELL             1\n",
       "46995   75510  0.004   ADBE             1\n",
       "50772   60871  0.003    ADI             1\n",
       "51405   75607  0.002   SYMC             1\n",
       "52769   86158 -0.002   CTSH             1\n",
       "55688   93002 -0.006   AVGO             1\n",
       "56632   86580 -0.011   NVDA             1\n",
       "57788   59328 -0.012   INTC             1\n",
       "61565   10696 -0.012   FISV             1\n",
       "61967   92611 -0.013      V             1\n",
       "65389   85963 -0.013   BRCM             1\n",
       "66920   15488 -0.014   PYPL             1\n",
       "68516   53613 -0.017     MU             1\n",
       "70176   86979 -0.018   JNPR             1\n",
       "70934   14593 -0.020   AAPL             1\n",
       "74711   90215 -0.022    CRM             1\n",
       "77377   12456 -0.025    MMI             1\n",
       "77441   15707 -0.027    HPE             1\n",
       "78319   16279 -0.028    VSM             1\n",
       "78443   91233 -0.030     MA             1\n",
       "81865   22779 -0.039    MOT             1\n",
       "82874   22779 -0.039    MSI             1\n",
       "83499   13447 -0.054    NOW             1\n",
       "84132   22293 -0.055    GLW             1\n",
       "87005   13628 -0.067   WDAY             1\n",
       "87638   88873 -0.094    GPN             1\n",
       "88040   93091 -0.144    AOL             1"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fut_ret = 'ret_5'\n",
    "grouped = tech_largeCap_ret.groupby('permno')\n",
    "\n",
    "tech_largeCap_correlation = grouped.apply(lambda x: x['moribvol'].corr(x[fut_ret])).dropna()\n",
    "tech_largeCap_correlation = tech_largeCap_correlation.sort_values(ascending=False).reset_index()\n",
    "tech_largeCap_correlation = tech_largeCap_correlation.rename(columns={tech_largeCap_correlation.columns[-1]: 'corr'})\n",
    "\n",
    "tech_largeCap_correlation = tech_largeCap_correlation.merge(tech_largeCap_ret[['ticker', 'permno', \"russellgroup\"]], on='permno')\n",
    "tech_largeCap_correlation = tech_largeCap_correlation.drop_duplicates()\n",
    "tech_largeCap_correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "50eb7ff9",
   "metadata": {
    "id": "61c0d1c7",
    "outputId": "e2beb2d1-6f41-4a86-964c-d07368171254"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2007-01-03 00:00:00')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Timestamp('2021-12-31 00:00:00')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_tech_tic = [\"QCOM\", \"ADSK\", \"FSLR\", \"MSFT\", \"AMD\", \"ORCL\", \"INTU\", \"WU\", \"LRCX\", \"TXN\", \"CSCO\"]\n",
    "selected_tech_ret = retail_ret[retail_ret['tic'].isin(selected_tech_tic)]\n",
    "\n",
    "# Earliest date of public trades\n",
    "min(selected_tech_ret['date'])\n",
    "\n",
    "# latest date of public trades\n",
    "max(selected_tech_ret['date'])\n",
    "\n",
    "# Check if their lengths are consistent\n",
    "assert selected_tech_ret.groupby([\"permno\", \"tic\"]).size().nunique() == 1\n",
    "\n",
    "# Sort by date \n",
    "selected_tech_ret = selected_tech_ret.sort_values(['date','tic'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fe684f3",
   "metadata": {},
   "source": [
    "## Data Merge with Technical Indicators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "38c96cbf",
   "metadata": {
    "id": "b3c3ce28",
    "outputId": "3fd8b3c7-74be-4c91-d1af-3ed2c2c8ab8d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (4074, 8)\n",
      "Successfully added technical indicators\n",
      "Successfully added turbulence index\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (4074, 8)\n",
      "Successfully added technical indicators\n",
      "Successfully added turbulence index\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (4074, 8)\n",
      "Successfully added technical indicators\n",
      "Successfully added turbulence index\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (4074, 8)\n",
      "Successfully added technical indicators\n",
      "Successfully added turbulence index\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (4074, 8)\n",
      "Successfully added technical indicators\n",
      "Successfully added turbulence index\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (4074, 8)\n",
      "Successfully added technical indicators\n",
      "Successfully added turbulence index\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (4074, 8)\n",
      "Successfully added technical indicators\n",
      "Successfully added turbulence index\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (4074, 8)\n",
      "Successfully added technical indicators\n",
      "Successfully added turbulence index\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (4074, 8)\n",
      "Successfully added technical indicators\n",
      "Successfully added turbulence index\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (4074, 8)\n",
      "Successfully added technical indicators\n",
      "Successfully added turbulence index\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (4074, 8)\n",
      "Successfully added technical indicators\n",
      "Successfully added turbulence index\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>tic</th>\n",
       "      <th>day</th>\n",
       "      <th>macd</th>\n",
       "      <th>boll_ub</th>\n",
       "      <th>boll_lb</th>\n",
       "      <th>rsi_30</th>\n",
       "      <th>cci_30</th>\n",
       "      <th>dx_30</th>\n",
       "      <th>close_30_sma</th>\n",
       "      <th>close_60_sma</th>\n",
       "      <th>turbulence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007-01-03</td>\n",
       "      <td>37.760</td>\n",
       "      <td>38.770</td>\n",
       "      <td>36.790</td>\n",
       "      <td>25.494</td>\n",
       "      <td>24184800</td>\n",
       "      <td>QCOM</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000</td>\n",
       "      <td>27.696</td>\n",
       "      <td>24.442</td>\n",
       "      <td>100.000</td>\n",
       "      <td>66.667</td>\n",
       "      <td>100.000</td>\n",
       "      <td>25.494</td>\n",
       "      <td>25.494</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007-01-04</td>\n",
       "      <td>37.790</td>\n",
       "      <td>39.400</td>\n",
       "      <td>37.400</td>\n",
       "      <td>26.644</td>\n",
       "      <td>26164600</td>\n",
       "      <td>QCOM</td>\n",
       "      <td>3</td>\n",
       "      <td>0.026</td>\n",
       "      <td>27.696</td>\n",
       "      <td>24.442</td>\n",
       "      <td>100.000</td>\n",
       "      <td>66.667</td>\n",
       "      <td>100.000</td>\n",
       "      <td>26.069</td>\n",
       "      <td>26.069</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007-01-05</td>\n",
       "      <td>38.900</td>\n",
       "      <td>38.940</td>\n",
       "      <td>37.870</td>\n",
       "      <td>26.331</td>\n",
       "      <td>18403600</td>\n",
       "      <td>QCOM</td>\n",
       "      <td>4</td>\n",
       "      <td>0.023</td>\n",
       "      <td>27.346</td>\n",
       "      <td>24.967</td>\n",
       "      <td>78.028</td>\n",
       "      <td>39.846</td>\n",
       "      <td>100.000</td>\n",
       "      <td>26.156</td>\n",
       "      <td>26.156</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2007-01-08</td>\n",
       "      <td>38.750</td>\n",
       "      <td>38.750</td>\n",
       "      <td>38.000</td>\n",
       "      <td>26.038</td>\n",
       "      <td>13545200</td>\n",
       "      <td>QCOM</td>\n",
       "      <td>0</td>\n",
       "      <td>0.011</td>\n",
       "      <td>27.105</td>\n",
       "      <td>25.149</td>\n",
       "      <td>64.356</td>\n",
       "      <td>15.586</td>\n",
       "      <td>100.000</td>\n",
       "      <td>26.127</td>\n",
       "      <td>26.127</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2007-01-09</td>\n",
       "      <td>38.500</td>\n",
       "      <td>38.650</td>\n",
       "      <td>37.840</td>\n",
       "      <td>25.753</td>\n",
       "      <td>15948700</td>\n",
       "      <td>QCOM</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.007</td>\n",
       "      <td>26.963</td>\n",
       "      <td>25.141</td>\n",
       "      <td>54.675</td>\n",
       "      <td>-27.419</td>\n",
       "      <td>52.646</td>\n",
       "      <td>26.052</td>\n",
       "      <td>26.052</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4069</th>\n",
       "      <td>2023-03-03</td>\n",
       "      <td>48.990</td>\n",
       "      <td>49.360</td>\n",
       "      <td>48.820</td>\n",
       "      <td>49.280</td>\n",
       "      <td>15967000</td>\n",
       "      <td>CSCO</td>\n",
       "      <td>4</td>\n",
       "      <td>0.227</td>\n",
       "      <td>50.786</td>\n",
       "      <td>46.289</td>\n",
       "      <td>54.652</td>\n",
       "      <td>68.537</td>\n",
       "      <td>8.933</td>\n",
       "      <td>48.415</td>\n",
       "      <td>48.120</td>\n",
       "      <td>0.735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4070</th>\n",
       "      <td>2023-03-06</td>\n",
       "      <td>49.460</td>\n",
       "      <td>49.810</td>\n",
       "      <td>49.240</td>\n",
       "      <td>49.420</td>\n",
       "      <td>13878400</td>\n",
       "      <td>CSCO</td>\n",
       "      <td>0</td>\n",
       "      <td>0.261</td>\n",
       "      <td>50.859</td>\n",
       "      <td>46.295</td>\n",
       "      <td>55.075</td>\n",
       "      <td>91.294</td>\n",
       "      <td>14.243</td>\n",
       "      <td>48.503</td>\n",
       "      <td>48.140</td>\n",
       "      <td>0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4071</th>\n",
       "      <td>2023-03-07</td>\n",
       "      <td>49.570</td>\n",
       "      <td>49.760</td>\n",
       "      <td>48.630</td>\n",
       "      <td>48.910</td>\n",
       "      <td>17506600</td>\n",
       "      <td>CSCO</td>\n",
       "      <td>1</td>\n",
       "      <td>0.243</td>\n",
       "      <td>50.880</td>\n",
       "      <td>46.408</td>\n",
       "      <td>53.206</td>\n",
       "      <td>51.955</td>\n",
       "      <td>5.342</td>\n",
       "      <td>48.550</td>\n",
       "      <td>48.159</td>\n",
       "      <td>0.310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4072</th>\n",
       "      <td>2023-03-08</td>\n",
       "      <td>49.020</td>\n",
       "      <td>49.270</td>\n",
       "      <td>48.740</td>\n",
       "      <td>49.110</td>\n",
       "      <td>12055500</td>\n",
       "      <td>CSCO</td>\n",
       "      <td>2</td>\n",
       "      <td>0.243</td>\n",
       "      <td>50.920</td>\n",
       "      <td>46.495</td>\n",
       "      <td>53.841</td>\n",
       "      <td>42.690</td>\n",
       "      <td>5.342</td>\n",
       "      <td>48.593</td>\n",
       "      <td>48.167</td>\n",
       "      <td>0.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4073</th>\n",
       "      <td>2023-03-09</td>\n",
       "      <td>49.300</td>\n",
       "      <td>49.690</td>\n",
       "      <td>48.710</td>\n",
       "      <td>48.810</td>\n",
       "      <td>12494100</td>\n",
       "      <td>CSCO</td>\n",
       "      <td>3</td>\n",
       "      <td>0.216</td>\n",
       "      <td>50.853</td>\n",
       "      <td>46.747</td>\n",
       "      <td>52.730</td>\n",
       "      <td>41.736</td>\n",
       "      <td>10.809</td>\n",
       "      <td>48.621</td>\n",
       "      <td>48.180</td>\n",
       "      <td>0.106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>44814 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date   open   high    low  close    volume   tic  day   macd  \\\n",
       "0    2007-01-03 37.760 38.770 36.790 25.494  24184800  QCOM    2  0.000   \n",
       "1    2007-01-04 37.790 39.400 37.400 26.644  26164600  QCOM    3  0.026   \n",
       "2    2007-01-05 38.900 38.940 37.870 26.331  18403600  QCOM    4  0.023   \n",
       "3    2007-01-08 38.750 38.750 38.000 26.038  13545200  QCOM    0  0.011   \n",
       "4    2007-01-09 38.500 38.650 37.840 25.753  15948700  QCOM    1 -0.007   \n",
       "...         ...    ...    ...    ...    ...       ...   ...  ...    ...   \n",
       "4069 2023-03-03 48.990 49.360 48.820 49.280  15967000  CSCO    4  0.227   \n",
       "4070 2023-03-06 49.460 49.810 49.240 49.420  13878400  CSCO    0  0.261   \n",
       "4071 2023-03-07 49.570 49.760 48.630 48.910  17506600  CSCO    1  0.243   \n",
       "4072 2023-03-08 49.020 49.270 48.740 49.110  12055500  CSCO    2  0.243   \n",
       "4073 2023-03-09 49.300 49.690 48.710 48.810  12494100  CSCO    3  0.216   \n",
       "\n",
       "      boll_ub  boll_lb  rsi_30  cci_30   dx_30  close_30_sma  close_60_sma  \\\n",
       "0      27.696   24.442 100.000  66.667 100.000        25.494        25.494   \n",
       "1      27.696   24.442 100.000  66.667 100.000        26.069        26.069   \n",
       "2      27.346   24.967  78.028  39.846 100.000        26.156        26.156   \n",
       "3      27.105   25.149  64.356  15.586 100.000        26.127        26.127   \n",
       "4      26.963   25.141  54.675 -27.419  52.646        26.052        26.052   \n",
       "...       ...      ...     ...     ...     ...           ...           ...   \n",
       "4069   50.786   46.289  54.652  68.537   8.933        48.415        48.120   \n",
       "4070   50.859   46.295  55.075  91.294  14.243        48.503        48.140   \n",
       "4071   50.880   46.408  53.206  51.955   5.342        48.550        48.159   \n",
       "4072   50.920   46.495  53.841  42.690   5.342        48.593        48.167   \n",
       "4073   50.853   46.747  52.730  41.736  10.809        48.621        48.180   \n",
       "\n",
       "      turbulence  \n",
       "0          0.000  \n",
       "1          0.000  \n",
       "2          0.000  \n",
       "3          0.000  \n",
       "4          0.000  \n",
       "...          ...  \n",
       "4069       0.735  \n",
       "4070       0.028  \n",
       "4071       0.310  \n",
       "4072       0.058  \n",
       "4073       0.106  \n",
       "\n",
       "[44814 rows x 17 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime as dt\n",
    "\n",
    "tz = pytz.timezone(\"America/New_York\")\n",
    "start = tz.localize(dt(2007,1,3))\n",
    "end = tz.localize(dt.today())\n",
    "\n",
    "fe = FeatureEngineer(use_technical_indicator=True,\n",
    "                     tech_indicator_list = INDICATORS,\n",
    "                     use_turbulence=True,\n",
    "                     user_defined_feature = False)\n",
    "\n",
    "# yf_tech = YahooDownloader(start_date = start,\n",
    "#                           end_date = end,\n",
    "#                           ticker_list = selected_tech_tic).fetch_data()\n",
    "\n",
    "# # Stack\n",
    "# processed = fe.preprocess_data(yf_tech)\n",
    "\n",
    "processed = pd.DataFrame()\n",
    "for symbol in selected_tech_tic:\n",
    "    yf_symbol = YahooDownloader(start_date = start, end_date = end, ticker_list = [symbol]).fetch_data()\n",
    "    processed_symbol = fe.preprocess_data(yf_symbol)\n",
    "    processed = pd.concat([processed, processed_symbol], axis=0)\n",
    "\n",
    "processed = processed.copy()\n",
    "processed = processed.fillna(0)\n",
    "processed = processed.replace(np.inf,0)\n",
    "processed['date'] = pd.to_datetime(processed['date'])\n",
    "\n",
    "processed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "15bfe839",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['QCOM', 'ADSK', 'FSLR', 'MSFT', 'AMD', 'ORCL', 'INTU', 'WU',\n",
       "       'LRCX', 'TXN', 'CSCO'], dtype=object)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed['tic'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13ec1727",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge technical indicators with returns\n",
    "df_merged = pd.merge(retail_ret, processed, on=['tic', 'date'])\n",
    "df_merged.to_csv('data/merged.csv', index=False)  # set index=False to exclude row index from CSV file"
   ]
  }
 ],
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